# Keyphrase Generation for Scientific Document Retrieval

arXiv: https://arxiv.org/abs/2106.14726 · HTML: https://mlsanity.com/paper/2106.14726/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Modeling Coverage for Neural Machine Translation](https://mlsanity.com/paper/1601.04811/) (2016)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [Unsupervised Keyphrase Extraction with Multipartite Graphs](https://mlsanity.com/paper/1803.08721/) (2018)
- [Keyphrase Generation with Correlation Constraints](https://mlsanity.com/paper/1808.07185/) (2018)
- [Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models](https://mlsanity.com/paper/1904.09171/) (2019)
- [Keyphrase Annotation with Graph Co-Ranking](https://mlsanity.com/paper/1611.02007/) (2016)

## Built on by (most cited first, 23 of 23)

- [Representation Learning for Resource-Constrained Keyphrase Generation](https://mlsanity.com/paper/2203.08118/) (2022)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation](https://mlsanity.com/paper/2201.05302/) (2022)
- [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://mlsanity.com/paper/2212.10233/) (2022)
- [Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking](https://mlsanity.com/paper/2505.21815/) (2025)
- [Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2310.06374/) (2023)
- [KPDrop: Improving Absent Keyphrase Generation](https://mlsanity.com/paper/2112.01476/) (2021)
- [A Large-Scale Dataset for Biomedical Keyphrase Generation](https://mlsanity.com/paper/2211.12124/) (2022)
- [Unsupervised Domain Adaptation for Keyphrase Generation using Citation Contexts](https://mlsanity.com/paper/2409.13266/) (2024)
- [On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation](https://mlsanity.com/paper/2402.14052/) (2024)
- [Pre-Trained Language Models for Keyphrase Prediction: A Review](https://mlsanity.com/paper/2409.01087/) (2024)
- [One2set + Large Language Model: Best Partners for Keyphrase Generation](https://mlsanity.com/paper/2410.03421/) (2024)
- [Deep Keyphrase Completion](https://mlsanity.com/paper/2111.01910/) (2021)
- [MemSum-DQA: Adapting An Efficient Long Document Extractive Summarizer for Document Question Answering](https://mlsanity.com/paper/2310.06436/) (2023)
- [Neural Keyphrase Generation: Analysis and Evaluation](https://mlsanity.com/paper/2304.13883/) (2023)
- [MetaKP: On-Demand Keyphrase Generation](https://mlsanity.com/paper/2407.00191/) (2024)
- [Zero-Shot Keyphrase Generation: Investigating Specialized Instructions and Multi-Sample Aggregation on Large Language Models](https://mlsanity.com/paper/2503.00597/) (2025)
- [ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation](https://mlsanity.com/paper/2505.24219/) (2025)
- [Preface to the Special Issue of the TAL Journal on Scholarly Document Processing](https://mlsanity.com/paper/2506.03587/) (2025)
- [An Analysis of Datasets, Metrics and Models in Keyphrase Generation](https://mlsanity.com/paper/2506.10346/) (2025)
- [Evaluating the Homogeneity of Keyphrase Prediction Models](https://mlsanity.com/paper/2602.12989/) (2026)
- [A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization](https://mlsanity.com/paper/2604.01452/) (2026)
- [Self-Evolving Search Index](https://mlsanity.com/paper/2609.19656/) (2026)

## Abstract (arXiv)

Sequence-to-sequence models have lead to significant progress in keyphrase generation, but it remains unknown whether they are reliable enough to be beneficial for document retrieval. This study provides empirical evidence that such models can significantly improve retrieval performance, and introduces a new extrinsic evaluation framework that allows for a better understanding of the limitations of keyphrase generation models. Using this framework, we point out and discuss the difficulties encountered with supplementing documents with -- not present in text -- keyphrases, and generalizing models across domains. Our code is available at https://github.com/boudinfl/ir-using-kg